AI & MACHINE LEARNING

AI Doesn't Fix Broken Processes.
It Automates Them.

If your data is messy, AI will just help you make mistakes faster. We validate your data logic first — then build AI solutions that actually work in production.

0%
Of AI Projects Fail to Reach Production
0x
Avg Return per $1 Invested in AI
0-4 wk
Readiness Assessment Timeline
< 0 mo
From Assessment to Deployed Solution
THE PROBLEM

The AI Hype Trap

Every vendor and board member says you need AI. Most initiatives fail because they start with the technology instead of the problem. The result: six-figure pilots that never reach production.

Sound Familiar?

You've sat through vendor demos that feel like magic but can't explain how they'd work with your actual data. You've greenlit POCs that impressed in the boardroom but stalled in production. The gap between demo and deployment is where budgets go to die.

OUR ANSWER

Data Logic First, AI Second

We don't start with models. We start with your data, your workflows, and your actual bottlenecks. When AI is the right answer, we know exactly where to point it.

What This Looks Like

A structured readiness assessment before any model training. Data quality audits that expose gaps before they become expensive. Use-case scoring that separates real ROI from executive FOMO. Production-grade architecture from day one — not a demo that needs to be rebuilt.

AI Capabilities

Four disciplines, one team. We bring the full stack of AI expertise so you don’t have to assemble it yourself.

01
AGENTS

Agentic AI & Automation

Autonomous agents that handle multi-step workflows: customer support routing, data pipeline orchestration, document processing. Built with LangChain, Semantic Kernel, and CrewAI.

Learn More
02
LLM

LLM Integration & RAG

Secure integration of OpenAI/ChatGPT, Claude, and Gemini with your proprietary data via retrieval-augmented generation. Your knowledge base, not the internet.

Learn More
03
ML

Predictive Analytics & ML

Custom models for forecasting, anomaly detection, and recommendation engines. Trained on your data, deployed in your infrastructure.

Learn More
04
MLOPS

MLOps & AI Infrastructure

Model monitoring, drift detection, retraining pipelines, and A/B testing. Because a model that works in a notebook is not a product.

Learn More
BUILT, NOT BOUGHT

How We Build Agentic AI That Governs Itself

This is a production agentic pipeline. When a request arrives, it classifies intent, routes to a specialist agent, enforces guardrails, and verifies confidence before responding — with a full audit trail. No black boxes. No unvalidated outputs.

  1. 1Route — Classify intent and select the right specialist agent
  2. 2Guard — PII redaction and policy compliance before execution
  3. 3Execute — Agent reasons through the task with tools and retrieval
  4. 4Verify — Confidence scoring, audit logging, human escalation
See How This Works for Your Team
pipeline.py
1from agents import Router, Specialist, GuardrailChain
2from models import AzureGPT4o, EmbeddingModel
3from vectorstore import PineconeIndex
4

5router   = Router(model=AzureGPT4o, strategy="intent-classification")
6index    = PineconeIndex("knowledge-base")
7guardrails = GuardrailChain([
8    "pii_redaction", "policy_compliance", "prompt_injection"
9])
10

11async def handle_request(query: str, user_ctx: dict) -> dict:
12    # 1. Route — classify intent, select specialist agent
13    intent = await router.classify(query, context=user_ctx)
14    agent  = Specialist.for_intent(
15        intent, tools=["search", "calculate", "draft"],
16        memory_window=10,
17    )
18

19    # 2. Guard — PII redaction + policy check before execution
20    safe_query = await guardrails.run(
21        query, metadata={"department": user_ctx["dept"]},
22        on_fail="reject_with_reason",
23    )
24

25    # 3. Execute — agent reasons with tools + retrieval
26    sources = await index.query(safe_query, top_k=8, threshold=0.82)
27    result  = await agent.run(
28        safe_query, context=sources,
29        max_steps=5, timeout_s=30,
30    )
31

32    # 4. Verify — confidence gate + audit trail
33    if result.confidence < 0.85:
34        return escalate_to_human(result, reason="low_confidence")
35

36    await audit_log.record(
37        query=safe_query, intent=intent.label,
38        confidence=result.confidence, sources=sources.ids,
39    )
40

41    return {"answer": result.output, "confidence": result.confidence,
42            "sources": sources.citations, "audit_id": result.trace_id}

OUR APPROACH

We build AI that survives contact with reality.

Data readiness before model selection

The best model in the world can't fix bad data. We audit your data quality, governance, and pipeline integrity before writing a single line of training code.

Start narrow, prove value, then expand

We pick one high-value, low-risk use case and deliver a working solution. That success funds the next initiative. No boil-the-ocean roadmaps.

Build for production, not for demos

Every POC we build uses production-grade architecture. When the pilot succeeds, deployment is a matter of scaling — not rebuilding from scratch.

Your data stays yours

We deploy in your cloud, use your security boundaries, and ensure your proprietary data never trains someone else's model.

Our AI/ML Toolkit

We work across the full AI stack — from foundation models to production infrastructure.

LLM Providers

ChatGPTAnthropic ClaudeGoogle GeminiAzure OpenAIAWS Bedrock

Frameworks & Orchestration

LangChainLlamaIndexSemantic KernelCrewAIHugging Face

Vector & Search

PineconeWeaviatepgvectorAzure AI SearchChroma

ML Platforms

Azure MLAWS SageMakerVertex AIMLflowWeights & Biases

Data Infrastructure

DatabricksSnowflakeApache AirflowdbtPostgreSQLRedis
Not sure if your data is ready for AI?

AI & Data Readiness Assessment

“I want to use AI but my data is a mess.”

We evaluate your systems, data quality, and governance to deliver a clear Go/No-Go readiness score and a prioritized roadmap to get you there.

Get Your Readiness Score
Have AI ideas but need proof they work?

AI Proof of Concept Sprint

“We have ideas for AI but don't know if they'll work with our data.”

A 3-4 week working POC against your real data, with accuracy metrics, cost projections, and a clear feasibility assessment. Not a demo — a decision.

Explore POC Sprints
EXECUTIVE BRIEFING

The Executive's AI Cheat Sheet

Plain-English definitions for the terminology that matters.

01Artificial Intelligence (AI)

The umbrella term for software that can do things usually requiring human judgment — seeing, hearing, reasoning, deciding. When vendors say "AI-powered," it could mean anything from a rules engine to GPT-4.

AI is not one technology — it's a spectrum. On one end, you have simple rule-based automation (if X, then Y). On the other, you have deep learning systems that recognize patterns in massive datasets. Most business value comes from the middle: supervised machine learning models trained on your historical data to make predictions, and large language models that handle text-heavy tasks. The key question isn't "should we use AI?" — it's "which type of AI fits this specific problem?"

02Machine Learning (ML)

The math layer. Feed it historical data (sales from 5 years, maintenance logs, customer behavior) and it predicts the future — inventory needs, equipment failures, churn risk.

ML models learn patterns from your data and improve with feedback. Supervised learning uses labeled examples (this transaction was fraud, this one wasn't). Unsupervised learning finds hidden groupings you didn't know existed. The catch: ML is only as good as the data you feed it. Garbage in, confident garbage out. That's why we start every engagement with a data readiness assessment.

03Large Language Models (LLMs)

The language layer — GPT-4, Claude, Gemini. They read, write, and summarize text. Feed them support tickets, contracts, or internal docs, and they extract answers, draft responses, and surface insights.

LLMs are trained on vast amounts of text and excel at understanding context and nuance. But they have a critical limitation: they only know what they were trained on. For business use, you need RAG (see below) to connect them to your proprietary data. Without it, they'll confidently make things up — a phenomenon called "hallucination." Our implementations always include guardrails: citation requirements, confidence scoring, and human-in-the-loop review for high-stakes decisions.

04Retrieval-Augmented Generation (RAG)

How you make an LLM answer questions about your data — not the internet's. Instead of retraining the model, you give it access to your documents at query time. Think of it as giving GPT a library card to your company's knowledge base.

RAG works in three steps: (1) your documents are chunked and converted into mathematical representations (embeddings) stored in a vector database, (2) when a user asks a question, the system finds the most relevant chunks, and (3) those chunks are passed to the LLM as context with the question. The model answers based only on what it was given — not its training data. This is how you get accurate, citeable answers without the cost and risk of fine-tuning a model on your data.

05Agentic AI

AI that takes action, not just generates text. An agent can research, reason through multi-step problems, use tools, and execute workflows — think "AI employee" rather than "AI chatbot."

Traditional AI responds to a single prompt. Agentic AI breaks complex tasks into steps, decides which tools to use, handles errors, and adapts its approach. Example: an agent that receives a customer complaint, searches your knowledge base, checks order history, drafts a response, and escalates to a human if confidence is low — all without being explicitly programmed for each step. The frameworks (LangChain, CrewAI, Semantic Kernel) are maturing fast, but production deployment requires careful guardrails on what actions agents can take.

06MLOps

Why deploying a model is 20% of the work. MLOps is the discipline of keeping AI systems running, accurate, and improving after launch — monitoring, retraining, versioning, and governance.

A model that's 95% accurate on launch day will degrade as your data changes — this is called "model drift." MLOps catches drift before it impacts decisions. It includes: automated monitoring dashboards, data quality checks on incoming data, scheduled retraining pipelines, A/B testing for model updates, and audit trails for regulatory compliance. Most AI projects fail not at the build stage, but at the maintenance stage. MLOps is the difference between a successful pilot and a production system that delivers value for years.

Portfolio

Your data is either an asset or a liability. Let’s find out which.

Book a 30-minute AI readiness conversation. No pitch deck. No demos. Just an honest assessment of where AI can move your business forward.

Assess Your AI ReadinessExplore Software Development